Congestion is a world-wide problem in transportation. One major reason is random interruptions. The traffic network is inherently stochastic, and strong dependencies exist among traffic quantities, e.g., travel time, traffic speed, link volume. Information in stochastic networks can help with adaptive routing in terms of minimizing expected travel time or disutility. Routing in such networks is different from that in deterministic networks or when stochastic dependencies are not taken into account. This dissertation addresses the optimal routing problems, including the optimal a priori path problem and the optimal adaptive routing problem with different information scenarios, in stochastic and time-dependent networks with explicit consideration of the correlations between link travel time random variables. There are a number of studies in the literature addressing the optimal routing problems, but most of them ignore the correlations between link travel times. The consideration of the correlations makes the problem studied in this dissertation difficult, both conceptually and computationally. The optimal path finding problem in such networks is different from that in stochastic and time-dependent networks with no consideration of the correlations. This dissertation firstly provides an empirical study of the correlations between random link travel times and also verifies the importance of the consideration of the spatial and temporal correlations in estimating trip travel time and its reliability. It then shows that Bellman's principle of optimality or non-dominance is not valid due to the time-dependency and the correlations. A new property termed purity is introduced and an exact label-correcting algorithm is designed to solve the problem. With the fast advance of telecommunication technologies, real-time traffic information will soon become an integral part of travelers' route choice decision making. The study of optimal adaptive routing problems is thus timely and of great value. This dissertation studies the problems with a wide variety of information scenarios, including delayed global information, real-time local information, pre-trip global information, no online information, and trajectory information. It is shown that, for the first four partial information scenarios, Bellman's principle of optimality does not hold. A heuristic algorithm is developed and employed based on a set of necessary conditions for optimality. The same algorithm is showed to be exact for the perfect online information scenario. For optimal adaptive routing problem with trajectory information, this dissertation proves that, if the routing policy is defined in a similar way to other four information scenarios, i.e., the trajectory information is included in the state variable, Bellman's principle of optimality is valid. However, this definition results in a prohibitively large number of the states and the computation can hardly be carried out. The dissertation provides a recursive definition for the trajectory-adaptive routing policy, for which the information is not included in the state variable. In this way, the number of states is small, but Bellman's principle of optimality or non-dominance is invalid for a similar reason as in the optimal path problem. Again purity is introduced to the trajectory-adaptive routing policy and an exact algorithm is designed based on the concept of decreasing order of time.

Les réseaux de capteurs sans fil (RCSFs) sont constitués d’un grand nombre de noeuds de capteurs qui sont généralement alimentés par batterie et conçu pour fonctionner pendant une grande période. Les domaines d’application sont nombreux et variés, tel que le domaine environnementale, médicale et militaire.L’atout majeur de ce dispositif est un déploiement à grande échelle sans aucune maintenance. Les capteurs n’ont pas besoin d’une infrastructure établie pour parvenir à transmettre des données vitales à l’étude de l’environnement. Il est nécessaire également de garantir une bonne qualité de service, car les réseaux de capteurs sans fils doivent intégrer des mécanismes qui permettent aux utilisateurs de prolonger la durée de vie du réseau en entier, car chaque noeud est alimenté par une source d’énergie limitée et généralement irremplaçable.C’est pourquoi, il est nécessaire d’optimiser la consommation d’énergie à tous les niveaux de conception de ce type de réseau. Par conséquent, la minimisation de la consommation d’énergie est un facteur de conception des plus importants dans les réseaux de capteurs.Le but de cette thèse est étudier les différents techniques de routages existant dans un contexte sans fil multi-saut afin d’obtenir de meilleures performances. Nous portons notre étude sur les protocoles de routages les plus connus afin de proposer dans une deuxième partie un nouveau protocole de routage permettant d’optimiser la consommation d’énergie dans les réseaux de capteurs sans fil, en gardant une qualité de service optimale. / Wireless sensor networks (WSNs) are composed of a large number of sensor nodes that are typicallybattery-powered and designed to operate for a long period. Application areas are many and varied, such as the environmental field, medical and military.The major advantage of this device is a large-scale deployment without any maintenance. The sensors do not need to achieve an established infrastructure to transmit vital data to the study of the environment. It is also necessary to ensure good quality service, because without son sensor networks must incorporate mechanisms that allow users to extend the life of the entire network, as each node is supplied by a limited power source and generally irreplaceable. Therefore, it is necessary to optimize the power consumption at all levels of design of this type of network. Accordingly, minimization of power consumption is one of the most important design factors in sensor networks.The aim of this thesis is study the different existing routing techniques in a context without multi-hop son to get better performance. We carry our study of the most popular routing protocols to offer in a second part a new routing protocol for optimizing energy consumption without son sensor networks, keeping an optimal quality of service.

The focus of this thesis is to find, visualize and analyze the optimal flow of autonomous electric vehicles with charge constraints in urban traffic with respect to energy consumption. The traffic has been formulated as a static multi-commodity network flow problem, for which two different models have been implemented to handle the charge constraints. The first model uses a recursive algorithm to find the optimal solution fulfilling the charge constraints, while the second model discretizes the commodities’ battery to predetermined battery levels. An implementation of both methods is provided through simulations on scenarios of three different sizes. The results show that both methods are capable of representing the traffic flow with charge constraints, with limitations given by the size of the problem. In particular, the recursive model has the advantage of considering the charge as a continuous quantity. On the other hand the discretization of battery levels allows to handle charge constraint setups with higher complexity, that is when longer detours are needed to fulfill the charge constraints.

In this thesis, we consider optimal control problems arising in high-speed integrated communication networks with Quality of Service (QOS) constraints. Integrated networks are expected to carry a large variety of traffic sources with widely varying traffic characteristics and performance requirements. Broadly, the traffic sources fall into two categories: (a) real-time sources with specified performance criteria, like small end to end delay and loss probability (sources of this type are referred to as Type 1 sources below), and (b) sources that do not have stringent performance criteria and do not demand performance guarantees from the network - the so-called Best Effort Type sources (these are referred to as Type 2 sources below). From the network's point of view, Type 2 sources are much more "controllable" than Type 1 sources, in the sense that the Type 2 sources can be dynamically slowed down, stopped or speeded up depending on traffic congestion in the network, while for Type 1 sources, the only control action available in case of congestion is packet dropping. Carrying sources of both types in the same network concurrently while meeting the performance objectives of Type 1 sources is a challenge and raises the question of equitable sharing of resources. The objective is to carry as much Type 2 traffic as possible without sacrificing the performance requirements of Type 1 traffic. We consider simple models that capture this situation. Consider a network node through which two connections pass, one each of Types 1 and 2. One would like to maximize the throughput of the Type 2 connection while ensuring that the Type 1 connection's performance objectives are met. This can be set up as a constrained optimization problem that, however, is very hard to solve. We introduce a parameter b that represents the "cost" of buffer occupancy by Type 2 traffic. Since buffer space is limited and shared, a queued Type 2 packet means that a buffer position is not available for storing a Type 1 packet; to discourage the Type 2 connection from hogging the buffer, the cost parameter b is introduced, while a reward for each Type 2 packet coming into the buffer encourages the Type 2 connection to transmit at a high rate. Using standard on-off models for the Type 1 sources, we show how values can be assigned to the parameter b; the value depends on the characteristics of the Type 1 connection passing through the node, i.e., whether it is a Variable Bit Rate (VBR) video connection or a Continuous Bit Rate (CBR) connection etc. Our approach gives concrete networking significance to the parameter b, which has long been considered as an abstract parameter in reward-penalty formulations of flow control problems (for example, [Stidham '85]). Having seen how to assign values to b, we focus on the Type 2 connection next. Since Type 2 connections do not have strict performance requirements, it is possible to defer transmitting a Type 2 packet, if the conditions downstream so warrant. This leads to the question: what is the "best" transmission policy for Type 2 packets? Decisions to transmit or not must be based on congestion conditions downstream; however, the network state that is available at any instant gives information that is old, since feedback latency is an inherent feature of high speed networks. Thus the problem is to identify the best transmission policy under delayed feedback information. We study this problem in the framework of Markov Decision Theory. With appropriate assumptions on the arrivals, service times and scheduling discipline at a network node, we formulate our problem as a Partially Observable Controlled Markov Chain (PO-CMC). We then give an equivalent formulation of the problem in terms of a Completely Observable Controlled Markov Chain (CO-CMC) that is easier to deal with., Using Dynamic Programming and Value Iteration, we identify structural properties of an optimal transmission policy when the delay in obtaining feedback information is one time slot. For both discounted and average cost criteria, we show that the optimal policy has a two-threshold structure, with the threshold on the observed queue length depending, on whether a Type 2 packet was transmitted in the last slot or not. For an observation delay k > 2, the Value Iteration technique does not yield results. We use the structure of the problem to provide computable upper and lower bounds to the optimal value function. A study of these bounds yields information about the structure of the optimal policy for this problem. We show that for appropriate values of the parameters of the problem, depending on the number of transmissions in the last k steps, there is an "upper cut off" number which is a value such that if the observed queue length is greater than or equal to this number, the optimal action is to not transmit. Since the number of transmissions in the last k steps is between 0 and A: both inclusive, we have a stack of (k+1) upper cut off values. We conjecture that these (k + l) values axe thresholds and the optimal policy for this problem has a (k + l)-threshold structure. So far it has been assumed that the parameters of the problem are known at the transmission control point. In reality, this is usually not known and changes over time. Thus, one needs an adaptive transmission policy that keeps track of and adjusts to changing network conditions. We show that the information structure in our problem admits a simple adaptive policy that performs reasonably well in a quasi-static traffic environment. Up to this point, the models we have studied correspond to a single hop in a virtual connection. We consider the multiple hop problem next. A basic matter of interest here is whether one should have end to end or hop by hop controls. We develop a sample path approach to answer this question. It turns out that depending on the relative values of the b parameter in the transmitting node and its downstream neighbour, sometimes end to end controls are preferable while at other times hop by hop controls are preferable. Finally, we consider a routing problem in a high speed network where feedback information is delayed, as usual. As before, we formulate the problem in the framework of Markov Decision Theory and apply Value Iteration to deduce structural properties of an optimal control policy. We show that for both discounted and average cost criteria, the optimal policy for an observation delay of one slot is Join the Shortest Expected Queue (JSEQ) - a natural and intuitively satisfactory extension of the well-known Join the Shortest Queue (JSQ) policy that is optimal when there is no feedback delay (see, for example, [Weber 78]). However, for an observation delay of more than one slot, we show that the JSEQ policy is not optimal. Determining the structure of the optimal policy for a delay k>2 appears to be very difficult using the Value Iteration approach; we explore some likely policies by simulation.

This thesis deals with a path planning using cellular automata algorithms in a rectangular grid environment. Theoretical part starts with an overview of commonly used approaches for path planning and later on focuses on existing cellular automata solutions and capabilities in detail. Implemented cellular automata algorithms and the commonly used path planning algorithms are together with a map generator described in the practical part. Conclusion of this thesis contains results completed in a special application.

Observing the earth from above is a great way of understanding our world better. From space, many complex patterns and relationships on the ground can be identified through high-quality satellite data. The quality and availability of this data in combination with recent advancement in various deep learning techniques allows us to find these patterns more effectively then ever. In this thesis, we will analyze satellite imagery by using deep neural networks in an attempt to find road networks in different cities around the world. Once we have located networks of roads in the cities we will represent them as graphs and deploy the Dijkstra shortest path algorithm to find optimal routes within these networks. Having the ability to efficiently use satellite imagery for near real-time road detection and optimal route prediction has many possible applications, especially from a humanitarian and commercial point of view. For example, in the humanitarian realm, the frequency of natural disasters is unfortunately increasing due to climate change and the need for emergency real-time mapping for relief organisations in the case of a severe flood or similar is growing.  The state-of-the-art deep neural network models that will be implemented, compared and contrasted for this task are mainly based on the U-net and ResNet architectures. However, before introducing these architectures the reader will be given a comprehensive introduction and theoretical background of deep neural networks to distinctly formulate the mathematical groundwork. The final results demonstrates an overall strong model performance across different metrics and data sets, with the highest obtained IoU-score being approximately 0.7 for the segmentation task. For some models we can also see a high degree of similarity between the predicted optimal paths and the ground truth optimal paths. / Att betrakta jorden från ovan är ett bra tillvägagångsätt för att förstå vår egen värld bättre. Från rymden, många komplexa mönster och samband på marken går att urskilja genom hög-upplöst satellitdata. Kvalitén och tillgängligheten av denna data, i kombination med de senaste framstegen inom djupa inlärningstekniker, möjliggör oss att hissa dessa mönster mer effektivt än någonsin. I denna avhandling kommer vi analysera satellitbilder med hjälp av djupa neurala nätverk i ett försök att hitta nätverk av vägar i olika städer runtom i världen. Efter vi har lokaliserat dessa nätverk av vägar så kommer vi att representera nätverken som grafer och använda oss av Dijkstras algoritm för att hitta optimala rutter inom dessa nätverk.  Att ha förmågan att kunna effektivt använda sig av satellitbilder för att i nära realtid kunna identifiera vägar och optimala rutter har många möjliga applikationer. Speciellt ur ett humant och kommersiellt perspektiv. Exempelvis, inom det humanitära området, så ökar dessvärre frekvensen av naturkatastrofer på grund av klimatförändringar och därmed är behovet av nödkartläggning i realtid för hjälporganisationer större än någonsin. En effektiv nödkartläggning skulle exempelvis kunna underlätta enormt vid en allvarlig översvämning eller dylikt.  Dem toppmoderna djupa neurala nätverksmodellerna som kommer implementeras, jämföras och nyanseras för denna uppgift är i huvudsak baserad på U-net och ResNet arkitekturerna. Innan vi presenterar dessa arkitekturer i denna avhandling så kommer läsaren att få en omfattande teoretisk bakgrund till djupa neurala nätverk för att tydligt formulera dem matematiska grundpelarna. Dem slutgiltiga resultaten visar övergripande stark prestanda för samtliga av våra modeller. Både på olika datauppsättningar samt utvärderingsmått. Den högste IoU poängen som uppnås är cirka 0,7 och vi kan även se en hög grad av likhet mellan vissa av våra förutsagda optimala rutter och mark sanningens optimala rutter.

Given a grid of cells, each having a value indicating its cost per unit area, a variant of the least-cost path problem is to find a corridor of a specified width connecting two termini such that its cost-weighted area is minimized. A computationally efficient method exists for finding such corridors, but as is the case with conventional raster-based least-cost paths, their incremental orientations are limited to a fixed number of (typically eight orthogonal and diagonal) directions, and therefore, regardless of the grid resolution, they tend to deviate from those conceivable on the Euclidean plane. Additionally, these methods are limited to problems found on two-dimensional grids and ignore the ever-increasing availability and necessity of three-dimensional raster based geographic data. This thesis attempts to address the problems highlighted above by designing and testing least-cost corridor algorithms. First a method is proposed for solving the two-dimensional raster-based least-cost corridor problem with reduced distortion by adapting a distortion reduction technique originally designed for least-cost paths and applying it to an efficient but distortionprone least-cost corridor algorithm. The proposed method for distortion reduction is, in theory, guaranteed to generate no less accurate solutions than the existing one in polynomial time and, in practice, expected to generate more accurate solutions, as demonstrated experimentally using synthetic and real-world data. A corridor is then modeled on a threedimensional grid of cost-weighted cubic cells or voxels as a sequence of sets of voxels, called ‘neighborhoods,’ that are arranged in a 26-hedoral form, design a heuristic method to find a sequence of such neighborhoods that sweeps the minimum cost-weighted volume, and test its performance with computer-generated random data. Results show that the method finds a low-cost, if not least-cost, corridor with a specified width in a threedimensional cost grid and has a reasonable efficiency as its complexity is O(n2) where n is the number of voxels in the input cost grid and is independent of corridor width. A major drawback is that the corridor found may self-intersect, which is often not only an undesirable quality but makes the estimation of its cost-weighted volume inaccurate. / Med tanke på ett rutnät av celler, som vart och ett har ett värde som indikerar dess kostnad per areaenhet, är en variant av det billigaste banproblemet att hitta en korridor med en specificerad bredd som förbinder två terminaler så att dess kostnadsviktade område minimeras. Det finns en beräkningseffektiv metod för att hitta sådana korridorer, men som är fallet med konventionella rasterbaserade lägsta kostnadsspår är deras inkrementella orienteringar begränsade till ett fast antal (vanligtvis åtta ortogonala och diagonala) riktningar, och därför, oavsett nätupplösning tenderar de att avvika från de tänkbara på det euklidiska planet. Dessutom är dessa metoder begränsade till problem som finns i tvådimensionella nät och ignorerar den ständigt ökande tillgängligheten och nödvändigheten av tredimensionell rasterbaserad geografisk data. Denna avhandling försöker ta itu med problemen som belyses ovan genom att utforma och testa korridoralgoritmer till lägsta kostnad. Först föreslås en metod för att lösa det tvådimensionella rasterbaserade problemet med billigaste korridorer med minskad förvrängning genom att anpassa en distorsionsminskningsteknik som ursprungligen utformades för billigaste vägar och tillämpa den på en effektiv men distorsionsbenägen billigaste korridoralgoritm. Den föreslagna metoden för distorsionsminskning är i teorin garanterad att generera inte mindre exakta lösningar än den befintliga i polynomtid och i praktiken förväntas generera mer exakta lösningar, vilket demonstreras experimentellt med syntetiska och verkliga data. En korridor modelleras sedan på ett tredimensionellt rutnät av kostnadsvägda kubikceller eller voxels som en sekvens av uppsättningar av voxels, kallade "stadsdelar", som är ordnade i en 26-hedoral form, designar en heuristisk metod för att hitta en sekvens av sådana stadsdelar som sveper den lägsta kostnadsviktade volymen och testar dess prestanda med datorgenererade slumpmässiga data. Resultaten visar att metoden hittar en låg kostnad, om inte minst kostnad, korridor med en specificerad bredd i ett tredimensionellt kostnadsnät och har en rimlig effektivitet eftersom dess komplexitet är O (n2) där n är antalet voxlar i ingångskostnadsnätet och är oberoende av korridorbredd En stor nackdel är att korridoren som hittas kan korsa sig själv, vilket ofta inte bara är en oönskad kvalitet utan gör uppskattningen av dess kostnadsviktade volym felaktig. / <p>QC 20210309</p>